Light microscopy offers a unique window into the life and works of microbes and their interactions with hosts. Mere visualization of images, however, does not provide the quantitative information needed to reliably and accurately characterize phenotypes or test computational models of cellular processes, and is unfeasible in high-throughput screens. Algorithms that automatically extract biologically meaningful quantitative data from images are therefore an increasingly essential complement to the microscopes themselves. This paper reviews some of the computational methods developed to detect, segment and track cells, molecules or viruses, with an emphasis on their underlying assumptions, limitations, and the importance of validation.